Hyperspectral Remote Sensing from Spaceborne and Low Altitude Aerial/Drone-Based Platforms —  Differences in Approaches, Data Processing Methods, and Applications

doi:10.3390/books978-3-0365-9833-8

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https://mdpi.com/books/pdfview/book/8577

Contributor(s)

Pour, Amin Beiranvand (editor)

Guha, Arindam (editor)

Crispini, Laura (editor)

Chatterjee, Snehamoy (editor)

Language

English

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Abstract

This Special Issue, titled “Hyperspectral Remote Sensing from Spaceborne and Low-Altitude Aerial/Drone-Based Platforms—Differences in Approaches, Data Processing Methods, and Applications”, presents the latest achievements in the field of hyperspectral remote sensing data processing and its related applications. A total of 18 manuscripts, all of which were evaluated by professional Guest Editors and reviewers, were submitted for publication in this Special Issue. Subsequently, 11 of these submissions were deemed to be of a high quality (based on the standards set by Remote Sensing) and were revised, accepted, and published in this Special Issue.

URI

https://directory.doabooks.org/handle/20.500.12854/137422

Keywords

HySpex; hyperspectral image processing; classification; wetlands mapping; Arctic; AVIRIS-NG; base metal; continuum removed spectral bands; ground magnetic data; banded magnetite quartzite; multi-rangespectral feature fitting; relative band depth; water quality parameters inversion; machine learning; UAV-borne hyperspectral data; water quality mapping; deep learning; models; sago; copper exploration; BPNN; NFAHP; ASTER; geological data; mineral potential map; GHG concentration; industrial area; remote sensing sensor; UAV; mapping; geochemical exploration; remote sensing; image fusion; mineral exploration; lightning locating system; particle swarm optimization; VLF and VHF sensors; GPS antennas; lightning mapping; environmental monitoring; algal pigment estimation; linear regression; imaging systems; hyperspectral imaging; drones; UAVs; river algae; algal blooms; water quality; inland waters; remote sensing imagery; land use land cover; water yield; United Nations Sustainable Development Goal (UNSDG) 6; mining geochemistry; random forest; geochemical zonality; copper mineralization; mineral prospectivity mapping; n/a